Human beings use various channels and media to obtain information. Among sense organs of a human body, the eye plays the most important role as an entrance for perceiving and receiving information. According to statistics, it is known that about 70% of the perceived and received information is obtained through a sense of vision among the human senses (vision, hearing, smell, taste and touch).
In the past, the perceived and received information was generally transmitted through a medium of letter. However, with a rapid development of computers and transmission media, image information by an image media holds the most important part as an object and subject in information communications of a modern society.
As information communication techniques develop in an aspect of hardware, various techniques also develop to transmit image information more rapidly and effectively. And, many methods are disclosed to satisfy various objects of image processing.
The image processing includes feature extraction, image enhancement, image restoration, image reconstruction, image analysis, image recognition and image compression. In particular, in recent days, the image processing has an interest in a method for reducing noise that may occur during image compression and processing involved in transmission of image information or during transmission of image information.
Various methods were introduced to reduce noise according to purpose and utility, however conventional methods reduce noise only in consideration of simple average or deviation of pixels adjacent to an object pixel for noise reduction.
A mean filter used typically in reducing noise is cited as an instance.
TABLE 1127505725845523410 51534510 42493385961
As shown in the above Table 1, assuming that an input image is a 5×5 image and a pixel value is brightness (the larger a pixel value is, the higher brightness is), when a pixel located at the 3rd row and 3rd column from the upper left cell is an object pixel for mean filtering and a filtering area is a 3×3 matrix, a value of the object pixel, ‘10’ is converted into an average value of pixels located in the 3×3 matrix around and including the object pixel, i.e. (5+8+45+4+10+51+5+10+42)/9=20.
The matrix of image data experiences an abrupt change of brightness value in the 3rd and 4th columns, and thus the image has an edge. However, the conventional method decreases brightness of the 3rd column having relatively higher brightness and increases brightness of the 4th column having relatively lower brightness. As a result, sharpness of the edge is inevitably lowered.
And, because the conventional method uniformly uses simple average and location information of the adjacent pixels, incidental errors may continuously occur during calculation and further noise may occur even after application of an algorithm.
In other words, the conventional method disregards a unique information of a corresponding pixel such as brightness, hue or saturation, and converts a value of the corresponding pixel into an average value of adjacent pixels. In this case, noise is somehow reduced, however a pixel corresponding to an edge is also converted into the average value, which results in reduced sharpness of an image.
Synthetically judging, generally the conventional method does not consider information of a corresponding pixel, such as brightness, hue, edge or location when reducing noise or a false color of an image, and thus has a disadvantage of loss of the above-mentioned information.
Further, in the case of a digital image is generated using various contemporary digital cameras and mobile phones on the market, various erroneous information may occur to image information, for example noise occurring in setting a high ISO (International Standards Organization) value to increase sensitivity in a dark environment, noise caused by excessive compression or noise occurring due to dust of a lens or sensor (CMOS (Complementary Metal Oxide Semiconductor) or CCD (Charged Coupled Device)). Therefore, it requires to reduce noise and minimize the loss of a unique information of an image.